In comparison with a database management system (hereinafter called DBMS) which executes a processing for data store in a storage system, there is an increasing demand for a data processing system which performs real-time processing, in case where the object to be processed is data arriving from moment to moment.
For example, as for a store, it is an important subject to utilize a smart shelf system which is a system for grasping the stock of the store in real time by exhibiting products to which each RFID tag is added on a product exhibit shelf on which an RFID reader is installed and continuously reading the RFID tag by the RFID reader and to make good use of a situation of the stock that varies every moment for sales promotion.
For such a data processing system which defines data transmitted every moment as stream data and is suitable for the real time processing of the stream data, a stream data processing system is proposed and for this type of stream data processing system, a stream data processing system STREAM is known (for example, refer to “Query Processing, Resource Management, and Approximation in a Data Stream Management System” written by R. Motwani, J. Widom, 22rasu, 21abcock, S. Babu, M. Datar, G. Manku, C. Olston, J. Rosenstein, and R. Varma in Proc. of the 2003 Conf. on Innovative Data Systems Research (CIDR), January (a non-patent document 1)).
For example, real time store stock management can be realized as follows by utilizing the stream data processing system in the smart shelf system. The stream data processing system continuously receives store stock information that varies every moment from the smart shelf system.
The stream data processing system detects an event that a product is taken out of a shelf by a customer by comparing a store stock situation at each time at which a store stock situation is received and a store stock situation at the preceding time.
A terminal for displaying product information such as a display is installed in a position adjacent to a shelf and visible to a customer and when the stream data processing system detects that a product is taken out of the shelf, it displays the explanation of the taken product and its related information. The purchase by the customer of the product can be promoted by displaying when the customer takes the product out of the shelf.
As described above, the stream data processing system is suitable for a real time application that utilizes the result of the real time processing of successively input stream data such as data from the RFID reader and the sensor and financial information including a stock price trend.
Besides, a demand for processing a data set including data not necessarily correct such as data output from the RFID reader and the sensor, data including noise as a result of the failure of reading and a measurement error and web data registered by the public increases.
For this type of processing system, a system TRIO that handles the reliability of data in a relational database (RDB) is known (for example, refer to “Trio-One: Layering Uncertainty and Lineage on Conventional DBMS” written by M. Mutsuzaki, M. Theobald, A. de Keijzer, J. Widom, P. Agrawal, O. Benjelioun, A Das Sarma, R. Murthy, and T. Sugihara in Proceedings of the Third Biennial Conference on Innovative Data Systems Research (CIDR07), January, 2007 (a non-patent document 2)).
For example, for the eyewitness report of a traffic accident, information such as reliability that a car which caused an accident is A is 0.6 and reliability that the car is B is 0.4 and information such as reliability that a driver of the car A is “a” is 0.3 and reliability that the driver is “b” is 0.7 are stored in the database.
Candidates to which reliability is added of the driver who caused the accident can be acquired by matching the eyewitness report of the car which caused the accident and the eyewitness report of the driver with the database storing such information.
As described above, in the TRIO system, reliability-added data is queried and the reliability-added result of the processing can be acquired.
For a processing system when no sensor data exists at predetermined time, a system that computes the reliability at that time of sensor data at another time and outputs the sensor data and the reliability is known (for example, refer to JP-A No. 2006-268369).
Even if no sensor data exists at predetermined time when a request for the output of sensor data at the predetermined time is made from external equipment, the reliability at the predetermined time of sensor data at another time is computed, and the computed reliability and the sensor data at another time are output. Hereby, the sensor data processing side can handle the sensor data like synchronous data by referring to the reliability.